Abstract:
Scene transitions are integral to video editing, enabling seamless storytelling by connecting dis tinct video segments into a cohesive visual narrative. Despite advancements in video editing tools, automated generation of high-quality, context-aware transitions remains a challenge, par ticularly in cinematic and professional applications. Current systems often rely on predefined templates, lacking adaptability to the dynamic requirements of individual scenes. This study explores the development of a semi-synthetic framework for automated scene tran sition generation using state-of-the-art machine learning techniques. The research begins by identifying and analyzing various types of transitions commonly employed in filmmaking, includ ing fades, dissolves, match cuts, and wipes, along with their respective use cases. Preliminary work focuses on designing and implementing a conditional diffusion-based model, leveraging the SEINE framework, with fine-tuning performed on a curated dataset. This approach incorpo rates textual and visual conditioning to guide the generation of intermediate frames, aiming for smooth and contextually coherent transitions. The study has further laid a foundation by reviewing and identifying suitable evaluation metrics, including Frechet Video Distance (FVD), Video-SSIM, and appropriate variations of Mean Opin ion Score (MOS). Ongoing efforts involve refining the training process, conducting experiments, and benchmarking the model against existing tools. This research seeks to address critical gaps in automated video editing, contributing toward a robust, adaptive, and efficient solution for scene transition generation.
